Weakly Supervised Point Clouds Transformer for 3D Object Detection

The annotation of 3D datasets is required for semantic-segmentation and object detection in scene understanding. In this paper we present a framework for the weakly supervision of a point clouds transformer that is used for 3D object detection. The aim is to decrease the required amount of supervision needed for training, as a result of the high cost of annotating a 3D datasets. We propose an Unsupervised Voting Proposal Module, which learns randomly preset anchor points and uses voting network to select prepared anchor points of high quality. Then it distills information into student and teacher network. In terms of student network, we apply ResNet network to efficiently extract local characteristics. However, it also can lose much global information. To provide the input which incorporates the global and local information as the input of student networks, we adopt the self-attention mechanism of transformer to extract global features, and the ResNet layers to extract region proposals. The teacher network supervises the classification and regression of the student network using the pre-trained model on ImageNet. On the challenging KITTI datasets, the experimental results have achieved the highest level of average precision compared with the most recent weakly supervised 3D object detectors.Comment: International Conference on Intelligent Transportation Systems (ITSC), 202

Ultrasound-targeted microbubble destruction mediated herpes simplex virus-thymidine kinase gene treats hepatoma in mice

<p>Abstract</p> <p>Objective</p> <p>The purpose of the study was to explore the anti-tumor effect of ultrasound -targeted microbubble destruction mediated herpes simplex virus thymidine kinase (HSV-TK) suicide gene system on mice hepatoma.</p> <p>Methods</p> <p>Forty mice were randomly divided into four groups after the models of subcutaneous transplantation tumors were estabilished: (1) PBS; (2) HSV-TK (3) HSV-TK+ ultrasound (HSV-TK+US); (4) HSV-TK+ultrasound+microbubbles (HSV-TK+US+MB). The TK protein expression in liver cancer was detected by western-blot. Applying TUNEL staining detected tumor cell apoptosis. At last, the inhibition rates and survival time of the animals were compared among all groups.</p> <p>Results</p> <p>The TK protein expression of HSV-TK+MB+US group in tumor-bearing mice tissues were significantly higher than those in other groups. The tumor inhibitory effect of ultrasound-targeted microbubble destruction mediated HSV-TK on mice transplantable tumor was significantly higher than those in other groups (p < 0.05), and can significantly improve the survival time of tumor-bearing mice.</p> <p>Conclusion</p> <p>Ultrasound-targeted microbubble destruction can effectively transfect HSV-TK gene into target tissues and play a significant inhibition effect on tumors, which provides a new strategy for gene therapy in liver cancer.</p

An Edge Extraction Algorithm for Weld Pool Based on Component Tree

In order to realize the automation and intelligence of welding process, the visual sensor and image processing technology of weld pool edge feature has become one of the key points. During the course of gas metal arc welding (GMAW), since this kind of welding requires a larger current, it makes the arc very strong and products so many droplets transfer and spatter interference. Therefore it is so difficult to extract the edge of welding pool. A new edge extraction algorithm based on component tree is proposed in the paper. It can realize the image segmentation adaptively using local features, retain the useful edge effectively and remove the false edge and noise as well. The experiments show that this algorithm can get more accurate edge information

Ectopic Over-expression of Oncogene Pim-2 Induce Malignant Transformation of Nontumorous Human Liver Cell Line L02

In order to prove that ectopic over-expression of Pim-2 could induce malignant transformation of human liver cell line L02, three groups of cells were set up including human liver cell line L02 (L02), L02 cells transfected with Pim-2 gene (L02/Pim-2) and L02 cells transfected with empty-vector (L02/Vector). Pim-2 expression levels were detected. The morphology, proliferation level, apoptosis rate and migration ability of the cells were detected respectively. Then the cells were subcutaneously inoculated into athymic mice and the microstructures of the neoplasm were observed. Compared with the controls, Pim-2 expression levels were significantly higher in L02/Pim-2 cells (P<0.05), and their morphology had obvious malignant changes. They also showed a significantly increased proliferation rate (P<0.05) and migration capacity (P<0.05), as well as a significantly decreased apoptosis rate (P<0.05). Only the athymic mice inoculated with L02/Pim-2 cells could generate neoplasm, and the morphology of the neoplasm coincided with that of the hepatoma. The results manifest that ectopic Pim-2 gene could be stably expressed in L02/Pim-2 cells. Both the morphological and biological changes of L02/Pim-2 cells demonstrate the trend of malignant transformation. L02/Pim-2 cells could generate hepatoma in athymic mice. In conclusion, Pim-2 could induce malignant transformation of human liver cell line L02

A new biological visual cognitive behavioural modeling for video energy computing

As we all know, human vision is quite sensitive to abnormal behaviors, which is attributed to the discharging of the receptor cells in the brain visual cortex and the ensuing bioelectrical energy features. Inspired by this biological nature, this paper constructs a computing model to describe video dynamic energy, which can further improve the perception of machine vision to abnormal behaviors. Experiments show that this computing model can extract video energy features of abnormal behaviors under complex environment

Support vector machine filtering data aid on fatigue driving detection

This paper proposes an assumption that filtering out the confusing “awake” data from fatigue driving detection model promotes the accuracy of detection of “drowsy” status under real driving situation. Instead of focus on both “drowsy” and “awake” driving status, we set our first priority to alarm “drowsy” and temporarily ignore the accuracy of “awake” status recognition. The Support Vector Machine as a good classifier is employed for data filtering, provides more efficient training data and removes the data that may confuse the detection model. The results prove our assumption by 72% accuracy on “drowsy” recognition, which is higher than 38% recognition performed by detection without SVM filtering. In addition, the size of training samples after filtering for conducting detection model is extremely smaller than no filtering

Automatic Detection of Driver Fatigue Using Driving Operation Information for Transportation Safety

Fatigued driving is a major cause of road accidents. For this reason, the method in this paper is based on the steering wheel angles (SWA) and yaw angles (YA) information under real driving conditions to detect drivers’ fatigue levels. It analyzes the operation features of SWA and YA under different fatigue statuses, then calculates the approximate entropy (ApEn) features of a short sliding window on time series. Using the nonlinear feature construction theory of dynamic time series, with the fatigue features as input, designs a “2-6-6-3” multi-level back propagation (BP) Neural Networks classifier to realize the fatigue detection. An approximately 15-h experiment is carried out on a real road, and the data retrieved are segmented and labeled with three fatigue levels after expert evaluation, namely “awake”, “drowsy” and “very drowsy”. The average accuracy of 88.02% in fatigue identification was achieved in the experiment, endorsing the value of the proposed method for engineering applications